Storage area network (SAN) forecasting in a heterogeneous environment

ABSTRACT

The present invention provides an approach for SAN forecasting in a heterogeneous environment. Specifically, under the present invention capacity data on the heterogeneous environment is gathered. Capacity management techniques will then be used to analyze the SAN utilization, identify growth trends and patterns. Proactively, plans are made to account for these changes. Thereafter, a Capacity Planning Margin (CPM) will be applied to the forecast to reflect actual customer usage. The CPM adjusted forecasts will then be reviewed. Then, the SAN environment can be monitored by comparing actual vs. planned and return to adjust the forecast accordingly.

FIELD OF THE INVENTION

The present invention generally relates to SAN forecasting.Specifically, the present invention relates to SAN forecasting in aheterogeneous environment.

BACKGROUND OF THE INVENTION

In a homogenous environment, a SAN storage vendor may have tools toidentify storage usage down to the application level, such as IBM SANVolume Controller for IBM SAN equipment (IBM, IBM SAN, and related termsare trademarks of IBM Corp. in the United States and/or othercountries). As all allocation is known, forecasting can be performedfollowing standard capacity management techniques. However, in aheterogeneous environment with multiple SAN vendors, as is seen in mostoutsourcing contracts, each storage vendor's tools do not inter-operatewith other vendor's SAN, as their tool is seen as a competitiveadvantage.

Therefore, SAN environments are managed in a manual fashion and datacollection for what is installed and allocated are difficult to collect.Given the lack of tool support, or the desire of an outsourced customerto reduced billed SAN usage, a discrepancy exists between the amount ofstorage allocated and the amount of storage actually installed. However,this utilized, but unallocated storage must still be included foraccurate forecasts. In order to realize total cost of ownership,realized cost savings in newer technology an optimized plan forforecasting growth is critical in keeping cost down and avoiding.Unfortunately, no existing tool exists for providing such forecasting.In view of the foregoing there exists a need to a tool that solves atleast one of the deficiencies in the related art.

SUMMARY OF THE INVENTION

In general, the present invention provides an approach for SANforecasting in a heterogeneous environment that will aid in determiningfuture growth requirements in a timely fashion, and meeting thecustomer's SAN storage expectation. Specifically, under the presentinvention, capacity data on the heterogeneous environment is gathered.This includes, but is not limited to, current configuration (installedSAN), SAN allocations, and SAN utilization. Then, a capacity forecast isperformed by using capacity management techniques to analyze the SANutilization, identify growth trends and patterns. Proactively, plans aremade to account for these changes. Thereafter, a Capacity PlanningMargin (CPM) will be applied to the capacity forecast to reflect actualcustomer usage. The CPM value as applied to storage categories may beadjusted to reflect growth patterns, but the overall SAN environmentshould reflect the CPM valuation. The CPM adjusted forecasts will thenbe reviewed (e.g., with management and finance) for approval so that anyplanning and installation of additional storage devices to support theworkload can be performed. Then, the SAN environment can be monitored bycomparing actual vs. planned and return to adjust the forecastaccordingly.

A first aspect of the present invention provides a method forforecasting Storage Area Network (SAN) storage in a heterogeneousenvironment, comprising: gathering capacity data on the heterogeneousenvironment; performing a capacity forecast to analyze the SANutilization and to identify growth trends and patterns; calculating aCapacity Planning Margin (CPM); and applying the CPM to the capacityforecast.

A second aspect of the present invention provides a system forforecasting Storage Area Network (SAN) storage in a heterogeneousenvironment, comprising: a module gathering capacity data on theheterogeneous environment; a module for performing a capacity forecastto analyze the SAN utilization and to identify growth trends andpatterns; a module for calculating a Capacity Planning Margin (CPM); anda module applying the CPM to the capacity forecast.

A third aspect of the present invention provides a program productstored on a computer readable medium for forecasting Storage AreaNetwork (SAN) storage in a heterogeneous environment, the computerreadable medium comprising program code for causing a computer systemto: gather capacity data on the heterogeneous environment; perform acapacity forecast to analyze the SAN utilization and to identify growthtrends and patterns; calculate a Capacity Planning Margin (CPM); andapply the CPM to the capacity forecast.

A fourth aspect of the present invention provides a method for deployinga system for forecasting Storage Area Network (SAN) storage in aheterogeneous environment, comprising: providing a computerinfrastructure being operable to: gather capacity data on theheterogeneous environment; perform a capacity forecast to analyze theSAN utilization and to identify growth trends and patterns; andcalculate a Capacity Planning Margin (CPM); and apply the CPM to thecapacity forecast.

A fifth aspect of the present invention provides a computer-implementedbusiness method for forecasting Storage Area Network (SAN) storage in aheterogeneous environment, comprising: gathering capacity data on theheterogeneous environment; performing a capacity forecast to analyze theSAN utilization and to identify growth trends and patterns; calculatinga Capacity Planning Margin (CPM); and applying the CPM to the capacityforecast.

A sixth aspect of the present invention provides computer softwareembodied in a propagated signal for forecasting Storage Area Network(SAN) storage in a heterogeneous environment, the computer softwaremedium comprising instructions for causing a computer system to: gathercapacity data on the heterogeneous environment; perform a capacityforecast to analyze the SAN utilization and to identify growth trendsand patterns; calculate a Capacity Planning Margin (CPM); and apply theCPM to the capacity forecast.

A seventh aspect of the present invention provides a data processingsystem for forecasting Storage Area Network (SAN) storage in aheterogeneous environment, comprising: a memory medium havinginstructions; a bus coupled to the memory medium; and a processorcoupled to the bus that when executing the instructions causes the dataprocessing system to: gather capacity data on the heterogeneousenvironment; perform a capacity forecast to analyze the SAN utilizationand to identify growth trends and patterns; calculate a CapacityPlanning Margin (CPM); and apply the CPM to the capacity forecast.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other features of this invention will be more readilyunderstood from the following detailed description of the variousaspects of the invention taken in conjunction with the accompanyingdrawings in which:

FIG. 1 depicts a method flow diagram according to the present invention.

FIG. 2 depicts illustrative output according to the present invention.

FIG. 3 depicts a more specific computerized implementation according tothe present invention.

The drawings are not necessarily to scale. The drawings are merelyschematic representations, not intended to portray specific parametersof the invention. The drawings are intended to depict only typicalembodiments of the invention, and therefore should not be considered aslimiting the scope of the invention. In the drawings, like numberingrepresents like elements.

DETAILED DESCRIPTION OF THE INVENTION

For convenience, the Detailed Description of the Invention has thefollowing Sections:

I. General Description

II. Computerized Implementation

I. General Description:

As indicated above, the present invention provides an approach for SANforecasting in a heterogeneous environment that will aid in determiningfuture growth requirements in a timely fashion, and meeting thecustomer's SAN storage expectation. Specifically, under the presentinvention capacity data on the heterogeneous environment is gathered.This includes, but is not limited to, current configuration (installedSAN), SAN allocations, and SAN utilization. Then, a capacity forecast isperformed by using capacity management techniques to analyze the SANutilization, identify growth trends and patterns. Proactively, plans aremade to account for these changes. Thereafter, a Capacity PlanningMargin (CPM) will be applied to the capacity forecast to reflect actualcustomer usage. The CPM value as applied to storage categories may beadjusted to reflect growth patterns, but the overall SAN environmentshould reflect the CPM valuation. The CPM adjusted forecasts will thenbe reviewed (e.g., with management and finance) for approval so that anyplanning and installation of additional storage devices to support theworkload can be performed. Then, the SAN environment can be monitored bycomparing actual vs. planned and return to adjust the forecastaccordingly.

To this extent, normal capacity planning techniques are utilized todevelop forecast models based on known allocations, includingseasonality factors and multiple workload categories. This basis isadjusted by the new capacity function discussed herein. Along theselines the present invention includes the development of a key capacityfunction based on experiences with distributed storage. This function iscalled the Capacity Planning Margin (CPM) or CPM percentage (the LatentGrowth Factor). Where distributed storage growth is tied to a specificworkload within an environment and there is a large gap in the workloadforecasting information. The teachings recited herein help ensure thatthe impact of this gap was minimized.

With the Capacity Planning Margin (CPM) value one would be able toadjust that percentage so as to adjust the forecasts to support actualutilization. The CPM can generally be inversely compared to an iceberg.In the reference account, about two thirds of the environment is knownand the other third is hidden or unseen. This is due to the size of theenvironment and its complexity which makes it difficult to manage andgather information.

Before, the invention is discussed with reference to the Figs., somegeneral rules of CPM development will be given:

Monthly growth will be constant with a pattern.

There will be constant gap between what is allocated and what isinstalled.

It is wise to set a limit as to what the forecast will include.Forecasts are based on historical/BAU information. Typically set a limitto the size of new unknown requirements that want to be placed into theenvironment as an emergency. Document a rule that states that any newunknown requirement that is 8% above your monthly forecasted growth isout of scope of the aligned forecast. This way protection will be inplace from hidden requirements that become apparent.

Referring now to FIG. 1, a method flow diagram according to the presentinvention is shown. In step S1, capacity data on the heterogeneousenvironment is gathered. This includes, but is not limited to, currentconfiguration (installed SAN), SAN allocations, and SAN utilization. Instep S2, a capacity forecast is performed by utilizing capacitymanagement techniques to analyze the SAN utilization and to identifygrowth trends and patterns. Proactively, plans are made to account forthese changes. In step S3, the Capacity Planning Margin (CPM) iscalculated and applied to the capacity forecast. In general, thiscalculation is as follows:1−((Allocated SAN*Allocation Threshold)/SAN Install Base)Where:

i. Allocated SAN=a sum of all SAN storage allocated to each Category

ii. Allocation Threshold=a Maximum desired utilization level

iii. SAN Install Base=a sum of all Installed SAN Storage

For clarity, the below example is presented with the followingillustrative values:

SAN install base=500,000 GB.

Allocation Threshold=80%

Allocated SAN=400,000 GB

Using the algorithm set forth above, the CPM calculation would be asfollows:1−((400,000*0.80)/500,000)=1−(320,000/500,000)=1−0.64=0.36, or 36%.

Once the CPM is applied to the capacity forecast, the CPM adjustedforecasts will be reviewed in step S4 (e.g., with management andfinance) for approval, planning and installation of additional storagedevices to support the workload can be performed. Then in step S5 theSAN environment can be monitored by comparing actual vs. planned storageand return to adjust the forecast accordingly.

In addition, the present invention can provide output in the form ofcharts, memoranda, etc. describing the forecast. Referring to FIG. 2, anillustrative chart 20 is shown. In chart 20, line 24 shows the trendsfollowing standard capacity forecasting techniques. Line 22 shows theCPM adjusted trend, which more accurately matches reality, and allowsfor better planning of the real SAN environment. As can be seen theteachings herein provide improved SAN forecasting and utilization.

Among other things, the present invention:

1. Allows accurate SAN forecasting in heterogeneous environment withoutaccurate knowledge of all SAN usage.

2. Supports forecasting by overall enterprise or categories containedwithin the enterprise.

3. Minimizes panic buying due to proactive planning for the impact ofunknown requirements.

4. For outsourcing, provides accurate forecasts without full informationor support of the customers SAN usage.

5. Can be expanded to supporting NAS and Mainframe Storage.

II. Computerized Implementation

Referring now to FIG. 3, a computerized implementation 100 of thepresent invention is shown. As depicted, implementation 100 includescomputer system/register 104 deployed within a computer infrastructure102. This is intended to demonstrate, among other things, that thepresent invention could be implemented within a network environment(e.g., the Internet, a wide area network (WAN), a local area network(LAN), a virtual private network (VPN), etc.), or on a stand-alonecomputer system. In the case of the former, communication throughout thenetwork can occur via any combination of various types of communicationslinks. For example, the communication links can comprise addressableconnections that may utilize any combination of wired and/or wirelesstransmission methods. Where communications occur via the Internet,connectivity could be provided by conventional TCP/IP sockets-basedprotocol, and an Internet service provider could be used to establishconnectivity to the Internet. Still yet, computer infrastructure 102 isintended to demonstrate that some or all of the components ofimplementation 100 could be deployed, managed, serviced, etc. by aservice provider who offers to implement, deploy, and/or perform thefunctions of the present invention for others.

As shown, computer system/register 104 includes a processing unit 106, amemory 108, a bus 110, and device interfaces 112. Further, computersystem/register 104 is shown communicating with external devices 114 andstorage system 116 that communicate with bus via device interfaces. Ingeneral, processing unit 106 executes computer program code, such asforecasting program 124 which is stored in memory 108 and/or storagesystem 116. While executing computer program code, processing unit 106can read and/or write data to/from memory 108, storage system 116,and/or device interfaces 112. Bus 110 provides a communication linkbetween each of the components in computer system/register 104. Althoughnot shown, computer system/register 104 could also include I/Ointerfaces that communicate with: one or more external devices 114 suchas a kiosk, a checkout station, a keyboard, a pointing device, adisplay, etc.); one or more devices that enable a user to interact withcomputer system/register 104; and/or any devices (e.g., network card,modem, etc.) that enable computer system/register 104 to communicatewith one or more other computing devices.

Computer infrastructure 102 is only illustrative of various types ofcomputer infrastructures for implementing the invention. For example, inone embodiment, computer infrastructure 102 comprises two or morecomputing devices (e.g., a server cluster) that communicate over anetwork to perform the various process of the invention. Moreover,computer system/register 104 is only representative of various possiblecomputer systems that can include numerous combinations of hardware. Tothis extent, in other embodiments, computer system/register 104 cancomprise any specific purpose computing article of manufacturecomprising hardware and/or computer program code for performing specificfunctions, any computing article of manufacture that comprises acombination of specific purpose and general purpose hardware/software,or the like. In each case, the program code and hardware can be createdusing standard programming and engineering techniques, respectively.Moreover, processing unit 106 may comprise a single processing unit, orbe distributed across one or more processing units in one or morelocations, e.g., on a client and server. Similarly, memory 108 and/orstorage system 116 can comprise any combination of various types of datastorage and/or transmission media that reside at one or more physicallocations. Further, device interfaces 112 can comprise any module forexchanging information with one or more external devices 114. Stillfurther, it is understood that one or more additional components (e.g.,system software, math co-processing unit, etc.) not shown in FIG. 3 canbe included in computer system/register 104.

Storage system 116 can be any type of system capable of providingstorage for information under the present invention. To this extent,storage system 116 could include one or more storage devices, such as amagnetic disk drive or an optical disk drive. In another embodiment,storage system 116 includes data distributed across, for example, alocal area network (LAN), wide area network (WAN) or a storage areanetwork (SAN) (not shown). In addition, although not shown, additionalcomponents, such as cache memory, communication systems, systemsoftware, etc., may be incorporated into computer system/register 104.

Shown in memory 108 of computer system 104 is forecasting program 124,which a set of modules 126. The modules generally provide the functionsof the present invention as described herein. Specifically (among otherthings), set of modules 126 is configured to: gather capacity data onthe heterogeneous environment (shown as input 120); perform capacityforecasting by utilizing capacity management techniques to analyze theSAN utilization and to identify growth trends and patterns; calculateand apply a Capacity Planning Margin (CPM) to the capacity forecast;review the CPM adjusted forecasts (e.g., with management and finance)for approval, planning and installation of additional storage devices tosupport the workload; monitor the SAN environment by comparing actualvs. planned and return to adjust the forecast accordingly; generate andprovide corresponding output 122.

While shown and described herein as SAN forecasting in a heterogeneousenvironment, it is understood that the invention further providesvarious alternative embodiments. For example, in one embodiment, theinvention provides a computer-readable/useable medium that includescomputer program code to enable a computer infrastructure to provide SANforecasting in a heterogeneous environment. To this extent, thecomputer-readable/useable medium includes program code that implementseach of the various process of the invention. It is understood that theterms computer-readable medium or computer useable medium comprises oneor more of any type of physical embodiment of the program code. Inparticular, the computer-readable/useable medium can comprise programcode embodied on one or more portable storage articles of manufacture(e.g., a compact disc, a magnetic disk, a tape, etc.), on one or moredata storage portions of a computing device, such as memory 108 (FIG. 3)and/or storage system 116 (FIG. 3) (e.g., a fixed disk, a read-onlymemory, a random access memory, a cache memory, etc.), and/or as a datasignal (e.g., a propagated signal) traveling over a network (e.g.,during a wired/wireless electronic distribution of the program code).

In another embodiment, the invention provides a business method thatperforms the process of the invention on a subscription, advertising,and/or fee basis. That is, a service provider, such as a SolutionIntegrator, could offer to provide SAN forecasting in a heterogeneousenvironment. In this case, the service provider can create, maintain,support, etc., a computer infrastructure, such as computerinfrastructure 102 (FIG. 3) that performs the process of the inventionfor one or more customers. In return, the service provider can receivepayment from the customer(s) under a subscription and/or fee agreementand/or the service provider can receive payment from the sale ofadvertising content to one or more third parties.

In still another embodiment, the invention provides acomputer-implemented method for SAN forecasting in a heterogeneousenvironment. In this case, a computer infrastructure, such as computerinfrastructure 102 (FIG. 3), can be provided and one or more systems forperforming the process of the invention can be obtained (e.g., created,purchased, used, modified, etc.) and deployed to the computerinfrastructure. To this extent, the deployment of a system can compriseone or more of: (1) installing program code on a computing device, suchas computer system/register 104 (FIG. 3), from a computer-readablemedium; (2) adding one or more computing devices to the computerinfrastructure; and (3) incorporating and/or modifying one or moreexisting systems of the computer infrastructure to enable the computerinfrastructure to perform the process of the invention.

As used herein, it is understood that the terms “program code” and“computer program code” are synonymous and mean any expression, in anylanguage, code or notation, of a set of instructions intended to cause acomputing device having an information processing capability to performa particular function either directly or after either or both of thefollowing: (a) conversion to another language, code or notation; and/or(b) reproduction in a different material form. To this extent, programcode can be embodied as one or more of: an application/software program,component software/a library of functions, an operating system, a basicdevice system/driver for a particular computing and/or device, and thelike.

A data processing system suitable for storing and/or executing programcode can be provided hereunder and can include at least one processorcommunicatively coupled, directly or indirectly, to memory element(s)through a system bus. The memory elements can include, but are notlimited to, local memory employed during actual execution of the programcode, bulk storage, and cache memories that provide temporary storage ofat least some program code in order to reduce the number of times codemust be retrieved from bulk storage during execution. Input/output ordevice devices (including, but not limited to, keyboards, displays,pointing devices, etc.) can be coupled to the system either directly orthrough intervening device controllers.

Network adapters also may be coupled to the system to enable the dataprocessing system to become coupled to other data processing systems,remote printers, storage devices, and/or the like, through anycombination of intervening private or public networks. Illustrativenetwork adapters include, but are not limited to, modems, cable modemsand Ethernet cards.

The foregoing description of various aspects of the invention has beenpresented for purposes of illustration and description. It is notintended to be exhaustive or to limit the invention to the precise formdisclosed, and obviously, many modifications and variations arepossible. Such modifications and variations that may be apparent to aperson skilled in the art are intended to be included within the scopeof the invention as defined by the accompanying claims.

1. A method for forecasting Storage Area Network (SAN) storage in aheterogeneous environment, comprising: gathering capacity data on theheterogeneous environment; performing a capacity forecast to analyze theSAN utilization and to identify growth trends and patterns; andcalculating a Capacity Planning Margin (CPM), the calculating comprisingcalculating the following algorithm:1−((Allocated SAN*Allocation Threshold)/SAN Install Base) Where: i.Allocated SAN=a sum of all SAN storage allocated to each of a set ofcategories; ii. Allocation Threshold=a maximum desired utilizationlevel; iii. SAN Install base=a sum of all installed SAN storage; andapplying the CPM to the capacity forecast.
 2. The method of claim 1,further comprising: reviewing the capacity forecast after applying theCPM; and monitoring the heterogeneous environment for any variances inthe capacity forecast.
 3. The method of claim 1, further comprisinggenerating output of the forecasting.
 4. The method of claim 1, theoutput comprising at least one chart depicting storage trends.
 5. Asystem for forecasting Storage Area Network (SAN) storage in aheterogeneous environment, comprising: a module gathering capacity dataon the heterogeneous environment; a module for performing a capacityforecast to analyze the SAN utilization and to identify growth trendsand patterns; and a module for calculating a Capacity Planning Margin(CPM), the module for calculating configured to calculate the followingalgorithm:1−((Allocated SAN*Allocation Threshold)/SAN Install Base) Where: i.Allocated SAN=a sum of all SAN storage allocated to each of a set ofcategories; ii. Allocation Threshold=a maximum desired utilizationlevel; iii. SAN Install base=a sum of all installed SAN storage; and amodule applying the CPM to the capacity forecast.
 6. The system of claim5, further comprising: a module for reviewing the capacity forecastafter applying the CPM; and a module for monitoring the heterogeneousenvironment for any variances in the capacity forecast.
 7. The system ofclaim 5, further comprising a module for generating output of theforecasting.
 8. The system of claim 5, the output comprising at leastone chart depicting storage trends.
 9. A program product stored on acomputer readable medium for forecasting Storage Area Network (SAN)storage in a heterogeneous environment, the computer readable mediumcomprising program code for causing a computer system to: gathercapacity data on the heterogeneous environment; perform a capacityforecast to analyze the SAN utilization and to identify growth trendsand patterns; and calculate a Capacity Planning Margin (CPM) using thefollowing algorithm:1−((Allocated SAN*Allocation Threshold)/SAN Install Base) Where: i.Allocated SAN=a sum of all SAN storage allocated to each of a set ofcategories; ii. Allocation Threshold=a maximum desired utilizationlevel; iii. SAN Install base=a sum of all installed SAN storage; andapply the CPM to the capacity forecast.
 10. The program product of claim9, the computer readable medium further comprising program code forcausing the computer system to: review the capacity forecast afterapplying the CPM; and monitor the heterogeneous environment for anyvariances in the capacity forecast.
 11. The program product of claim 9,the computer readable medium further comprising program code for causingthe computer system to: generate output of the forecasting.
 12. Themethod of claim 9, the output comprising at least one chart depictingstorage trends.
 13. A method for deploying a system for forecastingStorage Area Network (SAN) storage in a heterogeneous environment,comprising: providing a computer infrastructure being operable to:gather capacity data on the heterogeneous environment; perform acapacity forecast to analyze the SAN utilization and to identify growthtrends and patterns; and calculate a Capacity Planning Margin (CPM)using the following algorithm:1−((Allocated SAN*Allocation Threshold)/SAN Install Base) Where: i.Allocated SAN=a sum of all SAN storage allocated to each of a set ofcategories; ii. Allocation Threshold=a maximum desired utilizationlevel; iii. SAN Install base=a sum of all installed SAN storage; andapply the CPM to the capacity forecast.
 14. The method of claim 13, thecomputer infrastructure being further operable to: review the capacityforecast after applying the CPM; and monitor the heterogeneousenvironment for any variances in the capacity forecast.
 15. The methodof claim 13, the computer infrastructure being further operable togenerate output of the forecasting.
 16. The method of claim 13, theoutput comprising at least one chart depicting storage trends.